Ling Luo 2015-08-31

Works in the past:

1.Finish training word embeddings via 5 models :

using EnWiki dataset(953M):

CBOW,Skip-Gram

using text8 dataset(95.3M):

CBOW,Skip-Gram,C&W,GloVe,LBL and Order(count-based)

2.Use tasks to measure quality of the word vectors with various dimensions(10~200):

word similarity

the TOEFL set:small dataset

analogy task:9K semantic and 10.5K syntactic analogy questions

text classification:IMDB dataset——pos&neg,use unlabeled dataset to train word embeddings

sentence-level sentiment classification (based on convolutional neural networks)

part-of-speech tagging

Works in this week:

semantic&syntactic analogy: try to use different similarity calculation methods

named entity recognition

focus on cnn